NBIC-ACS Stage 2 Canadian Forest Fire Weather Index - projected scenarios, 5%, 2% and 1% annual exceedance probabilities
Bibliographic record
Abstract
Canadian Forest Fire Weather Index (FWI) is a fire weather potential index that describes how current weather conditions and recent precipitation patterns could support a landscape fire. While originally was developed for boreal forests, it is now adapted for use in various regions and climates and include other vegetations like grasslands, broadleaf forest and shrublands. FWI calculations are based on Van Wagner & Pickett (1985) and dependent on air temperature, relative humidity, precipitation and wind speed. Returned values start from 0 but don’t have an upper bound constraint. Values above 200 were not anticipated to occur in Canada (Van Wagner (1987) but are regularly reported in Australia. The data provided here is unbounded. Here we provide predicted upper-bound FWI values across the Australian landscape, defined for a set of annual exceedance probabilities (AEP) modelled using extreme values analysis on more than 43 years of daily data. The FWI potential rasters represent reasonable worst case extreme conditions. Specifically, the rasters represent the FWI potential for given AEPs of 20%, 10%, 5%, 2% and 1%. These FWI AEPs are based on the projected weather timeseries developed by NBIC using the historical regional weather reanalysis dataset BARRA-R2 [1], and CMIP6-CCAM Regional Climate Models (RCM) [2]. The Regional Climate Models (RCM) considered are: • ACCESS ESM 1.5 • EC-Earth 3 • CMCC ESM2 • CNRM ESM 2.1 • NCAR CESM2 • NorESM2 MM The future climate change scenarios considered are: • Shared Socioeconomic Pathway (SSP) SSP 1-26: Sustainability • SSP 3-70: Regional Rivalry • SSP 3-70: Regional Rivalry, using wind speed from the baseline scenario The combination of RCMs and future climate change scenarios results in a suite of 18 projected FWI potential datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".